# TIGER: Temporally Improved Graph Entity Linker

arXiv: https://arxiv.org/abs/2410.09128 · HTML: https://mlsanity.com/paper/2410.09128/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources](https://mlsanity.com/paper/2011.14867/) (2020)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [AM-GCN: Adaptive Multi-channel Graph Convolutional Networks](https://mlsanity.com/paper/2007.02265/) (2020)
- [Scalable Zero-shot Entity Linking with Dense Entity Retrieval](https://mlsanity.com/paper/1911.03814/) (2019)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System](https://mlsanity.com/paper/2204.08807/) (2022)
- [DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN](https://mlsanity.com/paper/2108.11883/) (2021)
- [Complex Factoid Question Answering with a Free-Text Knowledge Graph](https://mlsanity.com/paper/2103.12876/) (2021)
- [TempEL: Linking Dynamically Evolving and Newly Emerging Entities](https://mlsanity.com/paper/2302.02500/) (2023)
- [SpEL: Structured Prediction for Entity Linking](https://mlsanity.com/paper/2310.14684/) (2023)
- [Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph](https://mlsanity.com/paper/2004.11529/) (2020)
- [NASTyLinker: NIL-Aware Scalable Transformer-based Entity Linker](https://mlsanity.com/paper/2303.04426/) (2023)

## Built on by (most cited first, 1 of 1)

- [KARAMEL: Knowledge-Aware Ranking for Multimodal Entity Linking in the Arts via Heterogeneous Graphs](https://mlsanity.com/paper/2610.16502/) (2026)

## Abstract (arXiv)

Knowledge graphs change over time, for example, when new entities are introduced or entity descriptions change. This impacts the performance of entity linking, a key task in many uses of knowledge graphs such as web search and recommendation. Specifically, entity linking models exhibit temporal degradation - their performance decreases the further a knowledge graph moves from its original state on which an entity linking model was trained. To tackle this challenge, we introduce TIGER: a Temporally Improved Graph Entity Linker. By incorporating structural information between entities into the model, we enhance the learned representation, making entities more distinguishable over time. The core idea is to integrate graph-based information into text-based information, from which both distinct and shared embeddings are based on an entity's feature and structural relationships and their interaction. Experiments on three datasets show that our model can effectively prevent temporal degradation, demonstrating a 16.24% performance boost over the state-of-the-art in a temporal setting when the time gap is one year and an improvement to 20.93% as the gap expands to three years. The code and data are made available at urlhttps://github.com/pengyu-zhang/TIGER-Temporally-Improved-Graph-Entity-Linker.